ML Engineer, Compliance & AML Analytics (Production)

Okx

Singapore

On-site

SGD 120,000 - 180,000

Full time

14 days+

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Benefits offered by this job

Competitive compensation

Education subsidy

Team events

Wellness allowances

Healthcare coverage

Job summary

OKX is seeking an experienced ML engineer to build and deploy compliance-focused machine learning systems at a global crypto exchange. You will own production ML infra, collaborate with data scientists and compliance experts, and push AI-assisted development with LLM tooling.

The role requires strong Python, MLOps, and data engineering capabilities, with a preference for fintech/crypto domain experience.

Qualifications

8+ years in ML engineering, data science, or related field.
Strong Python and ML frameworks; hands-on MLOps including deployment, monitoring, and CI/CD pipelines for ML workflows.
Experience with big data platforms such as Spark, Databricks, Hadoop, or MaxCompute, and comfort designing and optimising both batch and real‑time data pipelines that feed production ML systems.
Demonstrable fluency with AI‑assisted development: you use LLM coding tools before, you have built LLM‑integrated pipelines or automation workflows in a professional context, and you have a clear, practical view on where these tools genuinely improve engineering output and where they introduce risk.
Good working knowledge of ML fundamentals across supervised, unsupervised, and anomaly detection methods, and an understanding of how different approaches translate to compliance problem formulations.
Familiarity with explainability frameworks such as SHAP or LIME, and an appreciation for what model governance and auditability require in a regulated environment.
Good communication skills and a collaborative working style, with the ability to work effectively alongside data scientists, compliance domain experts, and engineers.
Experience in financial services, fintech, or a crypto exchange, particularly in AML, KYC/KYB, transaction monitoring, or a related compliance domain, is a meaningful advantage.
Familiarity with the crypto ecosystem, on‑chain data, blockchain analytics, or VASP regulatory frameworks is a plus and will give you a head start in understanding the data you will be working with.

Responsibilities

Design, build, and deploy ML models for compliance use cases including AML transaction monitoring, customer risk rating, KYC/KYB risk scoring, sanctions exposure detection, and SAR analytics, working closely with data scientists on model architecture and with data engineers on pipeline design.
Own the production infrastructure for compliance ML: feature pipelines, model serving, monitoring, drift detection, and retraining workflows.
Build and maintain internal ML tooling that the broader team depends on: reusable pipeline components, experiment tracking, model registries, and evaluation frameworks.
Apply AI-assisted coding and automation as a matter of course: using LLM tooling to accelerate development, building automated pipelines that reduce manual analytical work, and integrating LLM-based capabilities into compliance workflows such as SAR narrative assistance, alert summarisation, and investigative triage.
Work with data scientists to take research-stage models into production, reviewing feature logic, validating pipeline assumptions, and bridging the gap between a notebook and a deployment that a compliance team can rely on.
Collaborate with compliance and legal stakeholders to ensure models are explainable and documented to the standard required for internal governance and regulatory review.
Keep a close eye on developments in compliance‑relevant ML: graph neural networks for network‑based AML detection, anomaly detection approaches for novel typologies, and emerging LLM applications in regulated environments.